Research on SAR Image Lightweight Detection Based on Improved YOLOV8  

基于改进YOLOV8的SAR图像轻量化检测研究

作　　者：WANG Qing SI Zhan-jun 王庆;司占军(天津科技大学人工智能学院,天津300457)

机构地区：[1]School of Institute of Artificial Intelligence,Tianjin University of Science and Technology,Tianjin 300457,China

出　　处：《印刷与数字媒体技术研究》2025年第1期93-100,共8页Printing and Digital Media Technology Study

摘　　要：In recent years,with the development of synthetic aperture radar(SAR)technology and the widespread application of deep learning,lightweight detection of SAR images has emerged as a research direction.The ultimate goal is to reduce computational and storage requirements while ensuring detection accuracy and reliability,making it an ideal choice for achieving rapid response and efficient processing.In this regard,a lightweight SAR ship target detection algorithm based on YOLOv8 was proposed in this study.Firstly,the C2f-Sc module was designed by fusing the C2f in the backbone network with the ScConv to reduce spatial redundancy and channel redundancy between features in convolutional neural networks.At the same time,the Ghost module was introduced into the neck network to effectively reduce model parameters and computational complexity.A relatively lightweight EMA attention mechanism was added to the neck network to promote the effective fusion of features at different levels.Experimental results showed that the Parameters and GFLOPs of the improved model are reduced by 8.5%and 7.0%when mAP@0.5 and mAP@0.5:0.95 are increased by 0.7%and 1.8%,respectively.It makes the model lightweight and improves the detection accuracy,which has certain application value.近年来随着合成孔径雷达技术(SAR)的发展和深度学习应用的普及,SAR图像轻量化检测成为兴起的一个研究方向。其核心目标是在保证检测精度和可靠性的同时,降低计算和存储需求,使之成为实现快速响应和高效处理的理想选择。基于此,本研究提出一种基于YOLOv8的轻量化SAR图像舰船目标检测算法。首先,将骨干网络中的C2f融合ScConv设计C2f-Sc模块以减少了卷积神经网络中特征之间的空间冗余和通道冗余。同时,将Ghost模块引入颈部网络,有效降低模型参数量和计算复杂度。在颈部网络添加相对轻量级的EMA注意力机制,促进不同层次特征的有效融合。实验结果表明,改进后的模型在mAP@0.5、mAP@0.5:0.95分别提升了0.7%、1.8%的情况下,参数量及GFLOPs别减少了8.5%、7.0%。在做到模型轻量化同时提升了检测精度,具有一定的应用价值。

关 键 词：YOLOv8 Synthetic aperture radar image LIGHTWEIGHT Target detection 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]